!pip install pandas matplotlib
Requirement already satisfied: pandas in c:\users\p2112675\.conda\envs\tfsklearn\lib\site-packages (1.5.3) Collecting matplotlib Using cached matplotlib-3.6.3-cp38-cp38-win_amd64.whl (7.2 MB) Requirement already satisfied: python-dateutil>=2.8.1 in c:\users\p2112675\.conda\envs\tfsklearn\lib\site-packages (from pandas) (2.8.2) Requirement already satisfied: numpy>=1.20.3 in c:\users\p2112675\.conda\envs\tfsklearn\lib\site-packages (from pandas) (1.24.1) Requirement already satisfied: pytz>=2020.1 in c:\users\p2112675\.conda\envs\tfsklearn\lib\site-packages (from pandas) (2022.7.1) Collecting pyparsing>=2.2.1 Using cached pyparsing-3.0.9-py3-none-any.whl (98 kB) Requirement already satisfied: packaging>=20.0 in c:\users\p2112675\.conda\envs\tfsklearn\lib\site-packages (from matplotlib) (23.0) Collecting kiwisolver>=1.0.1 Using cached kiwisolver-1.4.4-cp38-cp38-win_amd64.whl (55 kB) Collecting pillow>=6.2.0 Using cached Pillow-9.4.0-cp38-cp38-win_amd64.whl (2.5 MB) Collecting cycler>=0.10 Using cached cycler-0.11.0-py3-none-any.whl (6.4 kB) Collecting contourpy>=1.0.1 Using cached contourpy-1.0.7-cp38-cp38-win_amd64.whl (162 kB) Collecting fonttools>=4.22.0 Using cached fonttools-4.38.0-py3-none-any.whl (965 kB) Requirement already satisfied: six>=1.5 in c:\users\p2112675\.conda\envs\tfsklearn\lib\site-packages (from python-dateutil>=2.8.1->pandas) (1.16.0) Installing collected packages: pyparsing, pillow, kiwisolver, fonttools, cycler, contourpy, matplotlib Successfully installed contourpy-1.0.7 cycler-0.11.0 fonttools-4.38.0 kiwisolver-1.4.4 matplotlib-3.6.3 pillow-9.4.0 pyparsing-3.0.9
conda install -c intel scikit-learn
Collecting package metadata (current_repodata.json): ...working... done
Solving environment: ...working... done
## Package Plan ##
environment location: C:\Users\p2112675\.conda\envs\tfsklearn
added / updated specs:
- scikit-learn
The following packages will be downloaded:
package | build
---------------------------|-----------------
ca-certificates-2022.10.11 | haa95532_0 164 KB intel
certifi-2022.9.24 | py38haa95532_0 158 KB intel
dpcpp-cpp-rt-2023.0.0 | intel_25922 2.1 MB intel
dpcpp_cpp_rt-2023.0.0 | intel_25922 20 KB intel
fortran_rt-2023.0.0 | intel_25922 20 KB intel
icc_rt-2023.0.0 | intel_25922 20 KB intel
impi_rt-2021.8.0 | intel_25543 9.1 MB intel
intel-cmplr-lib-rt-2023.0.0| intel_25922 16.1 MB intel
intel-cmplr-lic-rt-2023.0.0| intel_25922 48 KB intel
intel-fortran-rt-2023.0.0 | intel_25922 3.5 MB intel
intel-opencl-rt-2023.0.0 | intel_25922 92.9 MB intel
intel-openmp-2023.0.0 | intel_25922 3.2 MB intel
intelpython-2023.0.0 | 1 5 KB intel
joblib-1.0.1 | pyh3f38642_3 207 KB intel
mkl-2023.0.0 | intel_25930 178.8 MB intel
mkl-service-2.4.0 | py38h5809ae4_14 48 KB intel
mkl_fft-1.3.1 | py38ha0f7485_22 258 KB intel
mkl_random-1.2.2 | py38ha2798aa_22 379 KB intel
mkl_umath-0.1.1 | py38h82923ec_32 277 KB intel
numpy-1.22.3 | py38hf0956d0_5 4 KB intel
numpy-base-1.22.3 | py38he60c17a_5 5.7 MB intel
openssl-1.1.1q | h2bbff1b_0 5.7 MB intel
scikit-learn-1.1.1 | py38hd77b12b_0 7.5 MB intel
scipy-1.7.3 | py38h38b71fe_6 29.9 MB intel
six-1.16.0 | pyhd3eb1b0_1 19 KB intel
tbb-2021.8.0 | vc14_intel_25874 218 KB intel
tbb4py-2021.8.0 | py38_intel_25874 74 KB intel
threadpoolctl-2.2.0 | pyh0d69192_0 16 KB intel
------------------------------------------------------------
Total: 356.6 MB
The following NEW packages will be INSTALLED:
dpcpp-cpp-rt intel/win-64::dpcpp-cpp-rt-2023.0.0-intel_25922
dpcpp_cpp_rt intel/win-64::dpcpp_cpp_rt-2023.0.0-intel_25922
fortran_rt intel/win-64::fortran_rt-2023.0.0-intel_25922
icc_rt intel/win-64::icc_rt-2023.0.0-intel_25922
impi_rt intel/win-64::impi_rt-2021.8.0-intel_25543
intel-cmplr-lib-rt intel/win-64::intel-cmplr-lib-rt-2023.0.0-intel_25922
intel-cmplr-lic-rt intel/win-64::intel-cmplr-lic-rt-2023.0.0-intel_25922
intel-fortran-rt intel/win-64::intel-fortran-rt-2023.0.0-intel_25922
intel-opencl-rt intel/win-64::intel-opencl-rt-2023.0.0-intel_25922
intel-openmp intel/win-64::intel-openmp-2023.0.0-intel_25922
intelpython intel/win-64::intelpython-2023.0.0-1
joblib intel/noarch::joblib-1.0.1-pyh3f38642_3
mkl intel/win-64::mkl-2023.0.0-intel_25930
mkl-service intel/win-64::mkl-service-2.4.0-py38h5809ae4_14
mkl_fft intel/win-64::mkl_fft-1.3.1-py38ha0f7485_22
mkl_random intel/win-64::mkl_random-1.2.2-py38ha2798aa_22
mkl_umath intel/win-64::mkl_umath-0.1.1-py38h82923ec_32
numpy intel/win-64::numpy-1.22.3-py38hf0956d0_5
numpy-base intel/win-64::numpy-base-1.22.3-py38he60c17a_5
scikit-learn intel/win-64::scikit-learn-1.1.1-py38hd77b12b_0
scipy intel/win-64::scipy-1.7.3-py38h38b71fe_6
six intel/noarch::six-1.16.0-pyhd3eb1b0_1
tbb intel/win-64::tbb-2021.8.0-vc14_intel_25874
tbb4py intel/win-64::tbb4py-2021.8.0-py38_intel_25874
threadpoolctl intel/noarch::threadpoolctl-2.2.0-pyh0d69192_0
The following packages will be SUPERSEDED by a higher-priority channel:
ca-certificates pkgs/main::ca-certificates-2023.01.10~ --> intel::ca-certificates-2022.10.11-haa95532_0
certifi pkgs/main::certifi-2022.12.7-py38haa9~ --> intel::certifi-2022.9.24-py38haa95532_0
openssl pkgs/main::openssl-1.1.1s-h2bbff1b_0 --> intel::openssl-1.1.1q-h2bbff1b_0
Downloading and Extracting Packages
tbb4py-2021.8.0 | 74 KB | | 0%
tbb4py-2021.8.0 | 74 KB | ##1 | 22%
tbb4py-2021.8.0 | 74 KB | ####3 | 43%
tbb4py-2021.8.0 | 74 KB | ########6 | 86%
tbb4py-2021.8.0 | 74 KB | ########## | 100%
intel-cmplr-lib-rt-2 | 16.1 MB | | 0%
intel-cmplr-lib-rt-2 | 16.1 MB | | 0%
intel-cmplr-lib-rt-2 | 16.1 MB | | 0%
intel-cmplr-lib-rt-2 | 16.1 MB | 1 | 1%
intel-cmplr-lib-rt-2 | 16.1 MB | 1 | 2%
intel-cmplr-lib-rt-2 | 16.1 MB | 3 | 3%
intel-cmplr-lib-rt-2 | 16.1 MB | 6 | 7%
intel-cmplr-lib-rt-2 | 16.1 MB | #3 | 13%
intel-cmplr-lib-rt-2 | 16.1 MB | ##6 | 26%
intel-cmplr-lib-rt-2 | 16.1 MB | ####6 | 47%
intel-cmplr-lib-rt-2 | 16.1 MB | #####4 | 55%
intel-cmplr-lib-rt-2 | 16.1 MB | ######8 | 69%
intel-cmplr-lib-rt-2 | 16.1 MB | ########8 | 89%
intel-cmplr-lib-rt-2 | 16.1 MB | #########9 | 100%
intel-cmplr-lib-rt-2 | 16.1 MB | ########## | 100%
numpy-base-1.22.3 | 5.7 MB | | 0%
numpy-base-1.22.3 | 5.7 MB | | 0%
numpy-base-1.22.3 | 5.7 MB | ######1 | 61%
numpy-base-1.22.3 | 5.7 MB | #########7 | 97%
numpy-base-1.22.3 | 5.7 MB | ########## | 100%
intelpython-2023.0.0 | 5 KB | | 0%
intelpython-2023.0.0 | 5 KB | ########## | 100%
intelpython-2023.0.0 | 5 KB | ########## | 100%
dpcpp_cpp_rt-2023.0. | 20 KB | | 0%
dpcpp_cpp_rt-2023.0. | 20 KB | #######8 | 79%
dpcpp_cpp_rt-2023.0. | 20 KB | ########## | 100%
openssl-1.1.1q | 5.7 MB | | 0%
openssl-1.1.1q | 5.7 MB | | 0%
openssl-1.1.1q | 5.7 MB | ######2 | 63%
openssl-1.1.1q | 5.7 MB | ########## | 100%
openssl-1.1.1q | 5.7 MB | ########## | 100%
tbb-2021.8.0 | 218 KB | | 0%
tbb-2021.8.0 | 218 KB | 7 | 7%
tbb-2021.8.0 | 218 KB | ########## | 100%
mkl-service-2.4.0 | 48 KB | | 0%
mkl-service-2.4.0 | 48 KB | ###3 | 34%
mkl-service-2.4.0 | 48 KB | ########## | 100%
fortran_rt-2023.0.0 | 20 KB | | 0%
fortran_rt-2023.0.0 | 20 KB | #######8 | 79%
fortran_rt-2023.0.0 | 20 KB | ########## | 100%
intel-cmplr-lic-rt-2 | 48 KB | | 0%
intel-cmplr-lic-rt-2 | 48 KB | ###3 | 34%
intel-cmplr-lic-rt-2 | 48 KB | ########## | 100%
mkl_umath-0.1.1 | 277 KB | | 0%
mkl_umath-0.1.1 | 277 KB | 5 | 6%
mkl_umath-0.1.1 | 277 KB | ########## | 100%
scikit-learn-1.1.1 | 7.5 MB | | 0%
scikit-learn-1.1.1 | 7.5 MB | | 0%
scikit-learn-1.1.1 | 7.5 MB | ####9 | 49%
scikit-learn-1.1.1 | 7.5 MB | #########3 | 94%
scikit-learn-1.1.1 | 7.5 MB | ########## | 100%
intel-openmp-2023.0. | 3.2 MB | | 0%
intel-openmp-2023.0. | 3.2 MB | | 0%
intel-openmp-2023.0. | 3.2 MB | ########## | 100%
intel-openmp-2023.0. | 3.2 MB | ########## | 100%
impi_rt-2021.8.0 | 9.1 MB | | 0%
impi_rt-2021.8.0 | 9.1 MB | | 0%
impi_rt-2021.8.0 | 9.1 MB | ####1 | 41%
impi_rt-2021.8.0 | 9.1 MB | #######3 | 74%
impi_rt-2021.8.0 | 9.1 MB | #########3 | 93%
impi_rt-2021.8.0 | 9.1 MB | ########## | 100%
intel-fortran-rt-202 | 3.5 MB | | 0%
intel-fortran-rt-202 | 3.5 MB | | 0%
intel-fortran-rt-202 | 3.5 MB | #########5 | 96%
intel-fortran-rt-202 | 3.5 MB | ########## | 100%
mkl_random-1.2.2 | 379 KB | | 0%
mkl_random-1.2.2 | 379 KB | 4 | 4%
mkl_random-1.2.2 | 379 KB | ########## | 100%
mkl_random-1.2.2 | 379 KB | ########## | 100%
mkl_fft-1.3.1 | 258 KB | | 0%
mkl_fft-1.3.1 | 258 KB | 6 | 6%
mkl_fft-1.3.1 | 258 KB | ########## | 100%
dpcpp-cpp-rt-2023.0. | 2.1 MB | | 0%
dpcpp-cpp-rt-2023.0. | 2.1 MB | | 1%
dpcpp-cpp-rt-2023.0. | 2.1 MB | ########## | 100%
dpcpp-cpp-rt-2023.0. | 2.1 MB | ########## | 100%
icc_rt-2023.0.0 | 20 KB | | 0%
icc_rt-2023.0.0 | 20 KB | #######8 | 79%
icc_rt-2023.0.0 | 20 KB | ########## | 100%
ca-certificates-2022 | 164 KB | | 0%
ca-certificates-2022 | 164 KB | 9 | 10%
ca-certificates-2022 | 164 KB | ########## | 100%
mkl-2023.0.0 | 178.8 MB | | 0%
mkl-2023.0.0 | 178.8 MB | | 0%
mkl-2023.0.0 | 178.8 MB | 2 | 2%
mkl-2023.0.0 | 178.8 MB | 4 | 4%
mkl-2023.0.0 | 178.8 MB | 5 | 5%
mkl-2023.0.0 | 178.8 MB | 6 | 6%
mkl-2023.0.0 | 178.8 MB | 7 | 7%
mkl-2023.0.0 | 178.8 MB | 8 | 8%
mkl-2023.0.0 | 178.8 MB | # | 10%
mkl-2023.0.0 | 178.8 MB | #1 | 11%
mkl-2023.0.0 | 178.8 MB | #2 | 12%
mkl-2023.0.0 | 178.8 MB | #3 | 14%
mkl-2023.0.0 | 178.8 MB | #4 | 15%
mkl-2023.0.0 | 178.8 MB | #5 | 16%
mkl-2023.0.0 | 178.8 MB | #6 | 17%
mkl-2023.0.0 | 178.8 MB | #7 | 18%
mkl-2023.0.0 | 178.8 MB | #8 | 19%
mkl-2023.0.0 | 178.8 MB | #9 | 20%
mkl-2023.0.0 | 178.8 MB | ## | 21%
mkl-2023.0.0 | 178.8 MB | ##1 | 22%
mkl-2023.0.0 | 178.8 MB | ##2 | 23%
mkl-2023.0.0 | 178.8 MB | ##3 | 24%
mkl-2023.0.0 | 178.8 MB | ##4 | 25%
mkl-2023.0.0 | 178.8 MB | ##5 | 25%
mkl-2023.0.0 | 178.8 MB | ##6 | 26%
mkl-2023.0.0 | 178.8 MB | ##7 | 27%
mkl-2023.0.0 | 178.8 MB | ##8 | 28%
mkl-2023.0.0 | 178.8 MB | ##9 | 29%
mkl-2023.0.0 | 178.8 MB | ### | 30%
mkl-2023.0.0 | 178.8 MB | ###1 | 32%
mkl-2023.0.0 | 178.8 MB | ###3 | 33%
mkl-2023.0.0 | 178.8 MB | ###4 | 34%
mkl-2023.0.0 | 178.8 MB | ###4 | 35%
mkl-2023.0.0 | 178.8 MB | ###5 | 35%
mkl-2023.0.0 | 178.8 MB | ###5 | 36%
mkl-2023.0.0 | 178.8 MB | ###6 | 36%
mkl-2023.0.0 | 178.8 MB | ###6 | 36%
mkl-2023.0.0 | 178.8 MB | ###7 | 37%
mkl-2023.0.0 | 178.8 MB | ###7 | 38%
mkl-2023.0.0 | 178.8 MB | ###9 | 39%
mkl-2023.0.0 | 178.8 MB | #### | 40%
mkl-2023.0.0 | 178.8 MB | #### | 41%
mkl-2023.0.0 | 178.8 MB | ####2 | 42%
mkl-2023.0.0 | 178.8 MB | ####4 | 44%
mkl-2023.0.0 | 178.8 MB | ####5 | 45%
mkl-2023.0.0 | 178.8 MB | ####6 | 47%
mkl-2023.0.0 | 178.8 MB | ####8 | 48%
mkl-2023.0.0 | 178.8 MB | ####9 | 49%
mkl-2023.0.0 | 178.8 MB | ##### | 51%
mkl-2023.0.0 | 178.8 MB | #####1 | 51%
mkl-2023.0.0 | 178.8 MB | #####2 | 53%
mkl-2023.0.0 | 178.8 MB | #####3 | 54%
mkl-2023.0.0 | 178.8 MB | #####4 | 55%
mkl-2023.0.0 | 178.8 MB | #####6 | 56%
mkl-2023.0.0 | 178.8 MB | #####7 | 57%
mkl-2023.0.0 | 178.8 MB | #####8 | 59%
mkl-2023.0.0 | 178.8 MB | ###### | 61%
mkl-2023.0.0 | 178.8 MB | ######1 | 62%
mkl-2023.0.0 | 178.8 MB | ######2 | 63%
mkl-2023.0.0 | 178.8 MB | ######4 | 64%
mkl-2023.0.0 | 178.8 MB | ######6 | 66%
mkl-2023.0.0 | 178.8 MB | ######7 | 67%
mkl-2023.0.0 | 178.8 MB | ######8 | 68%
mkl-2023.0.0 | 178.8 MB | ####### | 70%
mkl-2023.0.0 | 178.8 MB | ####### | 71%
mkl-2023.0.0 | 178.8 MB | #######2 | 72%
mkl-2023.0.0 | 178.8 MB | #######4 | 74%
mkl-2023.0.0 | 178.8 MB | #######6 | 76%
mkl-2023.0.0 | 178.8 MB | #######7 | 77%
mkl-2023.0.0 | 178.8 MB | #######8 | 78%
mkl-2023.0.0 | 178.8 MB | #######8 | 79%
mkl-2023.0.0 | 178.8 MB | #######9 | 79%
mkl-2023.0.0 | 178.8 MB | ######## | 80%
mkl-2023.0.0 | 178.8 MB | ########1 | 81%
mkl-2023.0.0 | 178.8 MB | ########2 | 82%
mkl-2023.0.0 | 178.8 MB | ########3 | 83%
mkl-2023.0.0 | 178.8 MB | ########4 | 84%
mkl-2023.0.0 | 178.8 MB | ########4 | 85%
mkl-2023.0.0 | 178.8 MB | ########6 | 86%
mkl-2023.0.0 | 178.8 MB | ########7 | 87%
mkl-2023.0.0 | 178.8 MB | ########8 | 88%
mkl-2023.0.0 | 178.8 MB | ########9 | 89%
mkl-2023.0.0 | 178.8 MB | ######### | 90%
mkl-2023.0.0 | 178.8 MB | #########1 | 91%
mkl-2023.0.0 | 178.8 MB | #########2 | 92%
mkl-2023.0.0 | 178.8 MB | #########3 | 93%
mkl-2023.0.0 | 178.8 MB | #########4 | 94%
mkl-2023.0.0 | 178.8 MB | #########5 | 95%
mkl-2023.0.0 | 178.8 MB | #########6 | 96%
mkl-2023.0.0 | 178.8 MB | #########7 | 97%
mkl-2023.0.0 | 178.8 MB | #########8 | 98%
mkl-2023.0.0 | 178.8 MB | #########9 | 99%
mkl-2023.0.0 | 178.8 MB | ########## | 100%
scipy-1.7.3 | 29.9 MB | | 0%
scipy-1.7.3 | 29.9 MB | | 0%
scipy-1.7.3 | 29.9 MB | | 0%
scipy-1.7.3 | 29.9 MB | | 0%
scipy-1.7.3 | 29.9 MB | | 1%
scipy-1.7.3 | 29.9 MB | 1 | 1%
scipy-1.7.3 | 29.9 MB | 2 | 2%
scipy-1.7.3 | 29.9 MB | 3 | 4%
scipy-1.7.3 | 29.9 MB | 6 | 6%
scipy-1.7.3 | 29.9 MB | 7 | 8%
scipy-1.7.3 | 29.9 MB | #4 | 15%
scipy-1.7.3 | 29.9 MB | #7 | 17%
scipy-1.7.3 | 29.9 MB | ##4 | 24%
scipy-1.7.3 | 29.9 MB | ##7 | 28%
scipy-1.7.3 | 29.9 MB | ###2 | 32%
scipy-1.7.3 | 29.9 MB | #### | 40%
scipy-1.7.3 | 29.9 MB | ####4 | 45%
scipy-1.7.3 | 29.9 MB | #####3 | 54%
scipy-1.7.3 | 29.9 MB | #####8 | 59%
scipy-1.7.3 | 29.9 MB | ######3 | 63%
scipy-1.7.3 | 29.9 MB | ######9 | 69%
scipy-1.7.3 | 29.9 MB | #######3 | 74%
scipy-1.7.3 | 29.9 MB | ######## | 80%
scipy-1.7.3 | 29.9 MB | ########4 | 85%
scipy-1.7.3 | 29.9 MB | ######### | 90%
scipy-1.7.3 | 29.9 MB | #########5 | 95%
scipy-1.7.3 | 29.9 MB | ########## | 100%
scipy-1.7.3 | 29.9 MB | ########## | 100%
joblib-1.0.1 | 207 KB | | 0%
joblib-1.0.1 | 207 KB | 7 | 8%
joblib-1.0.1 | 207 KB | ########## | 100%
joblib-1.0.1 | 207 KB | ########## | 100%
intel-opencl-rt-2023 | 92.9 MB | | 0%
intel-opencl-rt-2023 | 92.9 MB | | 0%
intel-opencl-rt-2023 | 92.9 MB | 3 | 4%
intel-opencl-rt-2023 | 92.9 MB | 7 | 7%
intel-opencl-rt-2023 | 92.9 MB | # | 11%
intel-opencl-rt-2023 | 92.9 MB | #3 | 14%
intel-opencl-rt-2023 | 92.9 MB | #5 | 15%
intel-opencl-rt-2023 | 92.9 MB | #7 | 18%
intel-opencl-rt-2023 | 92.9 MB | #9 | 20%
intel-opencl-rt-2023 | 92.9 MB | ##1 | 22%
intel-opencl-rt-2023 | 92.9 MB | ##4 | 25%
intel-opencl-rt-2023 | 92.9 MB | ##7 | 28%
intel-opencl-rt-2023 | 92.9 MB | ##9 | 30%
intel-opencl-rt-2023 | 92.9 MB | ###1 | 32%
intel-opencl-rt-2023 | 92.9 MB | ###4 | 35%
intel-opencl-rt-2023 | 92.9 MB | ###6 | 36%
intel-opencl-rt-2023 | 92.9 MB | ###8 | 39%
intel-opencl-rt-2023 | 92.9 MB | ####1 | 41%
intel-opencl-rt-2023 | 92.9 MB | ####5 | 45%
intel-opencl-rt-2023 | 92.9 MB | ####7 | 47%
intel-opencl-rt-2023 | 92.9 MB | ####9 | 49%
intel-opencl-rt-2023 | 92.9 MB | #####3 | 53%
intel-opencl-rt-2023 | 92.9 MB | #####5 | 55%
intel-opencl-rt-2023 | 92.9 MB | #####7 | 57%
intel-opencl-rt-2023 | 92.9 MB | ###### | 60%
intel-opencl-rt-2023 | 92.9 MB | ######1 | 62%
intel-opencl-rt-2023 | 92.9 MB | ######4 | 65%
intel-opencl-rt-2023 | 92.9 MB | ######6 | 67%
intel-opencl-rt-2023 | 92.9 MB | ######8 | 69%
intel-opencl-rt-2023 | 92.9 MB | ####### | 71%
intel-opencl-rt-2023 | 92.9 MB | #######2 | 72%
intel-opencl-rt-2023 | 92.9 MB | #######3 | 74%
intel-opencl-rt-2023 | 92.9 MB | #######5 | 75%
intel-opencl-rt-2023 | 92.9 MB | #######7 | 77%
intel-opencl-rt-2023 | 92.9 MB | #######8 | 79%
intel-opencl-rt-2023 | 92.9 MB | ########1 | 81%
intel-opencl-rt-2023 | 92.9 MB | ########2 | 83%
intel-opencl-rt-2023 | 92.9 MB | ########4 | 84%
intel-opencl-rt-2023 | 92.9 MB | ########7 | 87%
intel-opencl-rt-2023 | 92.9 MB | ######### | 91%
intel-opencl-rt-2023 | 92.9 MB | #########2 | 92%
intel-opencl-rt-2023 | 92.9 MB | #########4 | 94%
intel-opencl-rt-2023 | 92.9 MB | #########7 | 97%
intel-opencl-rt-2023 | 92.9 MB | ########## | 100%
intel-opencl-rt-2023 | 92.9 MB | ########## | 100%
threadpoolctl-2.2.0 | 16 KB | | 0%
threadpoolctl-2.2.0 | 16 KB | ########## | 100%
threadpoolctl-2.2.0 | 16 KB | ########## | 100%
six-1.16.0 | 19 KB | | 0%
six-1.16.0 | 19 KB | ########5 | 86%
six-1.16.0 | 19 KB | ########## | 100%
certifi-2022.9.24 | 158 KB | | 0%
certifi-2022.9.24 | 158 KB | # | 10%
certifi-2022.9.24 | 158 KB | ### | 30%
certifi-2022.9.24 | 158 KB | #########1 | 91%
certifi-2022.9.24 | 158 KB | ########## | 100%
numpy-1.22.3 | 4 KB | | 0%
numpy-1.22.3 | 4 KB | ########## | 100%
numpy-1.22.3 | 4 KB | ########## | 100%
Preparing transaction: ...working... done
Verifying transaction: ...working... done
Executing transaction: ...working...
Windows 64-bit packages of scikit-learn can be accelerated using scikit-learn-intelex.
More details are available here: https://intel.github.io/scikit-learn-intelex
For example:
$ conda install scikit-learn-intelex
$ python -m sklearnex my_application.py
done
Note: you may need to restart the kernel to use updated packages.
==> WARNING: A newer version of conda exists. <==
current version: 4.12.0
latest version: 23.1.0
Please update conda by running
$ conda update -n base -c defaults conda
!conda install -c plotly -y plotly
Collecting package metadata (current_repodata.json): ...working... done
==> WARNING: A newer version of conda exists. <==
current version: 4.12.0
latest version: 23.1.0
Please update conda by running
$ conda update -n base -c defaults conda
Solving environment: ...working... done
## Package Plan ##
environment location: C:\Users\p2112675\.conda\envs\gpuenv
added / updated specs:
- plotly
The following packages will be downloaded:
package | build
---------------------------|-----------------
plotly-5.13.0 | py_0 7.0 MB plotly
tenacity-8.0.1 | py38haa95532_1 34 KB
------------------------------------------------------------
Total: 7.0 MB
The following NEW packages will be INSTALLED:
plotly plotly/noarch::plotly-5.13.0-py_0
tenacity pkgs/main/win-64::tenacity-8.0.1-py38haa95532_1
The following packages will be UPDATED:
ca-certificates 2022.10.11-haa95532_0 --> 2023.01.10-haa95532_0
certifi 2022.9.24-py38haa95532_0 --> 2022.12.7-py38haa95532_0
Downloading and Extracting Packages
plotly-5.13.0 | 7.0 MB | | 0%
plotly-5.13.0 | 7.0 MB | ########## | 100%
plotly-5.13.0 | 7.0 MB | ########## | 100%
tenacity-8.0.1 | 34 KB | | 0%
tenacity-8.0.1 | 34 KB | ####6 | 46%
tenacity-8.0.1 | 34 KB | ########## | 100%
tenacity-8.0.1 | 34 KB | ########## | 100%
Preparing transaction: ...working... done
Verifying transaction: ...working... done
Executing transaction: ...working... done
https://machinelearningmastery.com/impressive-applications-of-generative-adversarial-networks/
import tensorflow as tf
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from numpy.random import default_rng
from sklearn.manifold import TSNE
import plotly.express as px
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.cifar10.load_data()
assert x_train.shape == (50000, 32, 32, 3)
assert x_test.shape == (10000, 32, 32, 3)
assert y_train.shape == (50000, 1)
assert y_test.shape == (10000, 1)
label_mapping={
0:'airplane' ,
1:'automobile' ,
2:'bird' ,
3:'cat' ,
4:'deer' ,
5:'dog' ,
6:'frog' ,
7:'horse' ,
8:'ship' ,
9:'truck',}
n_classes = 100
eday_train = np.squeeze(y_train)
fig, axes = plt.subplots(10,10 , figsize = (16,16))
for i in range(10):
for j in range(10 ):
ax = axes[i,j]
pic = x_train[eday_train == i ][j]
ax.axis('off')
ax.imshow(pic)
ax.set_title(label_mapping[i])
plt.show()
tsne = TSNE(n_components=2, random_state=42, verbose = 1 , n_jobs =3 , learning_rate = 500)
num_images,height,width , channels = x_train.shape
flattened_images = x_train.reshape((num_images, height*width*channels ))
reduced = tsne.fit_transform(flattened_images )
reduced_df = pd.DataFrame(columns=['Componenent1', 'Componenent2', 'target'],
data=np.column_stack((reduced,
eday_train)))
# sns.scatterplot( data =reduced_df, x= 'Componenent1' , y = 'Componenent2' , hue = 'target' )
# eda_y_train = np.squeeze(y_train)
px.scatter(reduced_df, x='Componenent1', y='Componenent2', opacity = 0.1,
color='target' , width = 900 ,height= 500 ).update_layout( margin=dict(l=20, r=10, t=10, b=0) ).show()
C:\Users\p2112675\.conda\envs\tfsklearn\lib\site-packages\sklearn\manifold\_t_sne.py:795: FutureWarning: The default initialization in TSNE will change from 'random' to 'pca' in 1.2. warnings.warn(
[t-SNE] Computing 91 nearest neighbors... [t-SNE] Indexed 50000 samples in 0.137s... [t-SNE] Computed neighbors for 50000 samples in 90.760s... [t-SNE] Computed conditional probabilities for sample 1000 / 50000 [t-SNE] Computed conditional probabilities for sample 2000 / 50000 [t-SNE] Computed conditional probabilities for sample 3000 / 50000 [t-SNE] Computed conditional probabilities for sample 4000 / 50000 [t-SNE] Computed conditional probabilities for sample 5000 / 50000 [t-SNE] Computed conditional probabilities for sample 6000 / 50000 [t-SNE] Computed conditional probabilities for sample 7000 / 50000 [t-SNE] Computed conditional probabilities for sample 8000 / 50000 [t-SNE] Computed conditional probabilities for sample 9000 / 50000 [t-SNE] Computed conditional probabilities for sample 10000 / 50000 [t-SNE] Computed conditional probabilities for sample 11000 / 50000 [t-SNE] Computed conditional probabilities for sample 12000 / 50000 [t-SNE] Computed conditional probabilities for sample 13000 / 50000 [t-SNE] Computed conditional probabilities for sample 14000 / 50000 [t-SNE] Computed conditional probabilities for sample 15000 / 50000 [t-SNE] Computed conditional probabilities for sample 16000 / 50000 [t-SNE] Computed conditional probabilities for sample 17000 / 50000 [t-SNE] Computed conditional probabilities for sample 18000 / 50000 [t-SNE] Computed conditional probabilities for sample 19000 / 50000 [t-SNE] Computed conditional probabilities for sample 20000 / 50000 [t-SNE] Computed conditional probabilities for sample 21000 / 50000 [t-SNE] Computed conditional probabilities for sample 22000 / 50000 [t-SNE] Computed conditional probabilities for sample 23000 / 50000 [t-SNE] Computed conditional probabilities for sample 24000 / 50000 [t-SNE] Computed conditional probabilities for sample 25000 / 50000 [t-SNE] Computed conditional probabilities for sample 26000 / 50000 [t-SNE] Computed conditional probabilities for sample 27000 / 50000 [t-SNE] Computed conditional probabilities for sample 28000 / 50000 [t-SNE] Computed conditional probabilities for sample 29000 / 50000 [t-SNE] Computed conditional probabilities for sample 30000 / 50000 [t-SNE] Computed conditional probabilities for sample 31000 / 50000 [t-SNE] Computed conditional probabilities for sample 32000 / 50000 [t-SNE] Computed conditional probabilities for sample 33000 / 50000 [t-SNE] Computed conditional probabilities for sample 34000 / 50000 [t-SNE] Computed conditional probabilities for sample 35000 / 50000 [t-SNE] Computed conditional probabilities for sample 36000 / 50000 [t-SNE] Computed conditional probabilities for sample 37000 / 50000 [t-SNE] Computed conditional probabilities for sample 38000 / 50000 [t-SNE] Computed conditional probabilities for sample 39000 / 50000 [t-SNE] Computed conditional probabilities for sample 40000 / 50000 [t-SNE] Computed conditional probabilities for sample 41000 / 50000 [t-SNE] Computed conditional probabilities for sample 42000 / 50000 [t-SNE] Computed conditional probabilities for sample 43000 / 50000 [t-SNE] Computed conditional probabilities for sample 44000 / 50000 [t-SNE] Computed conditional probabilities for sample 45000 / 50000 [t-SNE] Computed conditional probabilities for sample 46000 / 50000 [t-SNE] Computed conditional probabilities for sample 47000 / 50000 [t-SNE] Computed conditional probabilities for sample 48000 / 50000 [t-SNE] Computed conditional probabilities for sample 49000 / 50000 [t-SNE] Computed conditional probabilities for sample 50000 / 50000 [t-SNE] Mean sigma: 620.533136 [t-SNE] KL divergence after 250 iterations with early exaggeration: 107.371880 [t-SNE] KL divergence after 1000 iterations: 4.127415
reduced_df.target=reduced_df.target.astype('int').apply(lambda x: label_mapping[x])
reduced_df
plt.figure( figsize=(16,9))
sns.scatterplot(data =reduced_df , x = 'Componenent1' , y = 'Componenent2' , hue = 'target' , palette= sns.color_palette())
plt.show()
Images are flattend and passed into tsne
Air planes are well seperated from the rest of the classes occupying hte top left corner
Automobile also spread out across the space
Truck and ship more distinct from the rest (occupying the bottom left hand corner), but overlap with each other
eda_y_train = np.squeeze(y_train)
counts = pd.Series(eda_y_train).apply(label_mapping.get).value_counts()
plt.figure(figsize = (10, 10))
plt.barh(counts.index, counts )
plt.title('Check for Class Imbalance')
plt.ylabel('Class')
plt.xlabel('Number of images')
plt.show()
fig, ax = plt.subplots(1,10, figsize = ( 32,10))
for idx, a in enumerate(ax):
img = np.mean(x_train[np.squeeze(y_train== idx)], axis =0 )/255
a.imshow(img)
a.set_title(label_mapping[idx])
a.axis('off')
m = umap.UMAP(n_components=2, random_state=42)
num_images,height,width , channels = x_train.shape
flattened_images = x_train.reshape((num_images, height*width*channels ))
reduced = m.fit_transform(flattened_images )
reduced_df = pd.DataFrame(columns=['Componenent1', 'Componenent2', 'target'],data=np.column_stack((reduced,y_train_20)))
reduced_df.target = reduced_df.target.astype('str')
# sns.scatterplot( data =reduced_df, x= 'Componenent1' , y = 'Componenent2' , hue = 'target' )
# eda_y_train = np.squeeze(y_train)
Get the pixel values by
(img -127.5)/127.5to get the images in the range of -1 and 1 so that the scale of real images is the same as the generated images passing through the last layer of tanh from -1 to 1 .
https://arxiv.org/abs/1706.08500
InceptionV3. https://arxiv.org/pdf/2206.10935.pdf#:~:text=The%20Kernel%20Inception%20Distance%20
Measures the Maximum Mean Discrepancy between the real and generated
The KID improves on the FID by relaxing the gaussian requirement as it is a non parametric test